#------------------------------------------------------------------------------------------------------------------------------
Version 2 MULTIVARIATE-conjugate (Zellner's g-prior, specify prior parameters in code)
#------------------------------------------------------------------------------------------------------------------------------
model{
	  #---------------------------------------------------------------
    # definition of prior parameters
	  c2 <- 25
	  # prior means
    for (j in 1:P){ mu.beta[j] <- 0.0 }
    # calculation of xtx
    for (i in 1:P){ for (j in 1:P){ 
       inverse.V[i,j] <- inprod( x[,i] , x[,j] )
    }}
    # calculation of the elements of prior precision matrix
    for(i in 1:P){ for (j in 1:P){
      prior.T[i,j] <- inverse.V[i,j] * tau /c2
    }}
	  #---------------------------------------------------------------
    # model's likelihood
    # ------------------
		for (i in 1:n){
		     x[i,1] <- 1.0
		     # specifying the constant term in the first column
		     time[i] ~ dnorm( mu[i], tau ) # stochastic componenent
		     # link and linear predictor
				 mu[i] <- inprod( beta[], x[i,] )
		}
		# prior distributions
    # ------------------
    # calculation of the inverse matrix of V
    # prior parameters
    # multivariate prior for the beta vector
    beta[1:P] ~ dmnorm( mu.beta[], prior.T[,] )
    # gamma prior for the precision
    tau    ~ dgamma( 0.01, 0.01 )  
    # deterministic calculation of variance 
    s2    <- 1/tau 
 	 s <-sqrt(s2)
 	 #	 
}

INITS
list( tau=1, beta=c(1, 0, 0) )


list( n=25, P=3, 
      time = c(16.68, 11.5, 12.03, 14.88, 13.75, 18.11,  8, 17.83, 
               79.24, 21.5, 40.33, 21, 13.5, 19.75, 24, 29, 15.35, 
               19, 9.5, 35.1, 17.9, 52.32, 18.75, 19.83, 10.75), 
      x=structure(.Data=c(NA,  7, 560, NA, 3, 220, NA, 3, 340, NA, 4,   80, 
        NA, 6, 150, NA,  7, 330, NA, 2, 110, NA, 7, 210, NA,30, 1460, 
        NA, 5, 605, NA, 16, 688, NA,10, 215, NA, 4, 255, NA, 6,  462, 
        NA, 9, 448, NA, 10, 776, NA, 6, 200, NA, 7, 132, NA, 3,   36, 
        NA,17, 770, NA, 10, 140, NA,26, 810, NA, 9, 450, NA, 8,  635, 
        NA, 4, 150), .Dim = c(25,3)) )


DATA (RECT.)
list(n=25, P=3)
time[]	x[,2]	x[,3]
 16.68  7	560
 11.5	  3	220
 12.03  3	340
 14.88  4	80
 13.75  6	150
 18.11  7	330
  8	    2	110
 17.83  7	210
79.24 30	1460
 21.5   5	605
 40.33 16	688
 21    10 215
 13.5   4	255
 19.75  6	462
 24	    9	448
 29	   10	776
 15.35  6	200
 19	    7	132
 9.5    3	36
 35.1	 17	770
 17.9	 10	140
 52.32 26	810
 18.75  9	450
 19.83  8	635
 10.75  4	150
END